Scene classification has become an active research area in remote sensing (RS) image interpretation. Recently, Transformer-based methods have shown great potential in modeling global semantic information and have been exploited in RS scene classification. In this letter, we propose a multi-level fusion Swin Transformer (MFST), which integrates a multi-level feature merging (MFM) module and an adaptive feature compression (AFC) module to further boost the performance for RS scene classification. The MFM module narrows the semantic gaps in multi-level features via patch merging in lower-level feature maps and lateral connections in the top-down pathway. The AFC module makes multi-level features have smaller dimensions and more coherent semantic information by adaptive channel reduction. We evaluate the proposed network on the aerial image dataset (AID) and NWPU-RESISC45 (NWPU) datasets, and the classification results reveal that the proposed network outperforms several state-of-the-art (SOTA) methods.

Deep learning (DL) has been introduced to change detection (CD) due to its powerful feature representation and robust generalization abilities. However, the application of large DL models has high computational complexity and massive storage requirements for achieving good performance. For disaster emergency response and other applications with high timeliness requirements, it is difficult to deploy large DL models on spaceborne edge devices with limited resources to achieve on-board CD processing. To address this limitation, a novel CD based on knowledge distillation (CDKD) method that combines prototypical contrastive (PC) distillation and channel-spatial-normalized (CSN) distillation is proposed. PC distillation represents the feature distribution by calculating the differences between the similarities of pixel features and their positive and negative prototypes, and improves the student model's detection ability in changed regions that have similar features to the background by mimicking the relative feature distribution. CSN distillation combines two distillation paradigms, channel normalization and spatial normalization, and guides the student model to comprehensively learn the knowledge contained in the output probabilities of the teacher model to accurately identify changed regions with complex shapes. The effectiveness and reliability of the proposed CDKD method are verified on three public remote sensing CD datasets, and extensive experiments and analyses show that the proposed CDKD method can be used to train lightweight models with comparable performance to that of large models.

Directly performing remote sensing scene classification (RSSC) on satellites can effectively relieve pressures on data downlinks. However, existing convolutional neural network (CNN)-based methods with dense multiplication operations require a vast resource overhead and can hardly be deployed on resource-limited space platforms. In this article, an all adder neural network (A ² NN) with a generative-based hybrid knowledge distillation (GHKD) training strategy is proposed to solve this problem. In contrast to traditional CNNs, the A ² NN discards all multiplication operations in the convolutional layers of the network and is instead completely composed of adder kernels; thus, this network has lower resource overheads than existing CNNs in hardware deployment. Compared with CNNs, the A ² NN has a considerably lower RSSC accuracy; fortunately, this can be mitigated with the proposed GHKD training strategy. This strategy combines a hybrid knowledge distillation method and a knowledge matching-based generative learning method. The hybrid knowledge distillation method utilizes the weight distribution knowledge in the adder neural network (ANN) to guide the high-performance classification knowledge in the CNN to be smoothly transferred to the A ² NN, which consists entirely of adder kernels and has a considerably different structure than the CNN. The knowledge matching-based generative learning method generates effective samples by matching the knowledge of the feature distribution of the real samples in the knowledge sources to augment the training database for hybrid knowledge distillation. Extensive experiments and analyses on six public RSSC datasets show that the proposed GHKD training strategy significantly improves the RSSC performance of the proposed A ² NN, achieving comparable performance to CNNs on most datasets.

Unsupervised domain adaptation (UDA) methods have become a research hotspot in remote sensing scene classification to reduce dependence on labeled samples. However, most current methods focus on extracting domain invariant features, ignoring the problem of large intra-class differences and the imbalanced sample numbers between categories in remote sensing images. To address these issues, we propose a remote sensing scene domain adaptive method based on middle-layer feature extraction and nuclear norm maximization (MFE-NM). In the MFE module, the middle-layer features of the feature extractor are randomly extracted and processed. Since the receptive field of the middle-layer features is smaller and the resolution is higher, the effective use of the middle-layer features can reduce the impact of image feature heterogeneity caused by large intra-class differences in remote sensing images. In addition, it can be concluded that the constrained nuclear norm can simultaneously improve the prediction diversity and discriminability of the model through theoretical derivation. Therefore, the NM module is proposed to solve the problem of reduced prediction diversity caused by entropy minimization methods when dealing with scene classification problems with imbalanced sample numbers between categories. Extensive experiments and analyses on three public remote sensing datasets demonstrate the effectiveness and competitiveness of our proposed method.